Machine Learning in JavaScript with TensorFlow.js

• Fundamentals of Machine Learning in JavaScript: Gain a solid understanding of machine learning principles and how they can be implemented using JavaScript and TensorFlow.js.
• Using Pre-Trained Models: Learn to integrate pre-trained models for image, audio, and gesture recognition into web applications with minimal code.
• Custom Model Training: Acquire skills to train custom models using webcam input and optimize them for better accuracy and performance.
• Transfer Learning Techniques: Discover how to leverage transfer learning to enhance models by adapting pre-trained models to new tasks.
• Creative Applications of Machine Learning: Explore creative coding projects that utilize machine learning for various recognition tasks such as object, gesture, and audio recognition.
• Real-Time Model Deployment: Understand how to deploy and run machine learning models directly in the browser, enabling real-time predictions and interactions.

The "Machine Learning in JavaScript with TensorFlow.js" course by Charlie Gerard offers an in-depth exploration of using machine learning in web applications. The course covers the basics of machine learning, the use of pre-trained models, and techniques for training custom models using JavaScript. Learners will also explore transfer learning and build creative applications for image, gesture, and audio recognition. With hands-on projects, the course demonstrates how to integrate machine learning into real-time browser applications, making it accessible to frontend developers interested in AI.

• Frontend developers eager to explore machine learning applications in web development.
• JavaScript developers interested in integrating AI into their projects.
• Creatives and technologists looking to expand their skills with TensorFlow.js.
• Individuals curious about practical applications of machine learning in web environments.

• Basic knowledge of JavaScript and web development.
• Familiarity with HTML and CSS.
• No prior machine learning experience is required, but beneficial.

Introduction

• Overview of machine learning projects with TensorFlow.js.
• Introduction to image, audio, and gesture recognition applications.

Machine Learning Overview

• Understanding the differences between machine learning and AI.
• Explanation of supervised, unsupervised, semi-supervised, and reinforcement learning.

Pre-Trained Models

• Integrating pre-trained models for tasks like image recognition and text classification.
• Building a simple application using TensorFlow.js and the coco-ssd module.

Using Webcam and Face Detection

• Expanding projects to use webcam input for real-time object and face detection.
• Implementing face detection models and visualizing results on canvas elements.

Transfer Learning

• Introduction to transfer learning and using the Teachable Machine for custom models.
• Training and deploying models to recognize new tasks using transfer learning techniques.

Training Models in the Browser

• Building a project to train models directly in the browser using TensorFlow.js.
• Recording and processing data, creating model layers, and optimizing for predictions.

Image Classification Project

• Setting up an image classification project to detect shapes drawn on a canvas.
• Collecting and preparing training data, building, and testing datasets.

Training and Testing Models

• Training models with image data and optimizing for accuracy.
• Saving model data and using it for predictions in browser-based applications.

Wrapping Up

• Recap of course projects and their connection to real-world applications.
• Additional resources for continued learning and exploration of TensorFlow.js.